A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

Abstract

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

“…We are also working on using the Actor model of concurrency [19], leveraging multi-threaded computation for massive live traffic streams. It will be useful for supporting the sensing needs of a wide range of researches [20][21][22][23][24][25][26][27][28][29][30] and applications [31][32][33][34][35][36][37][38][39][40]. Finally, experiments with more massive datasets are needed to study the robustness of our system at a large scale, and improve the prediction accuracy of the less performing application classes.…”

Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

“…When the number epochs were increased to 50, the highest accuracy the CNN achieved for validation was 97.59%, with an average of 64.17% per test dataset. The overall accuracy of the CNN was essentially sufficient; however, it failed, as shown in Figure 6c, to classify three classes, namely Black Seed with 50 ppm of nutrients and Flandria with 50 and 100 ppm of nutrients, in the tested datasets (Table 1) indicating that the classifier could not differentiate between all of the classes with smaller sample sizes [33,50]. A detailed comparison of all model performances was generated with 15 epochs of evolution (Figures 5 and 6).…”

“…The present study employed Keras, an inbuilt augmentation technique proposed by Sokolova and Lapalme [33]. Due to size and processing power limitations, a randomly selected batch size of 16 images from the training dataset was used.…”

Apply Machine Learning with Smartphones to Detect and Recommend the Right Treatment for Plant Diseases

“…Plant illness, explicitly on leaves, has turned into a wellspring of serious worry in the horticultural area as it generally makes harms crops and thus a decrease in the amount and nature of food creation. Nonetheless, expedient revelation and exact distinguishing proof of these illnesses could help with growing early treatment approaches while altogether decreasing monetary misfortune [1]. Ranchers in most non-industrial nations on the planet utilize visual review procedures for plant illness recognizable proof, which should be possible either by the rancher or by rural specialists.…”